SpatialKWD: Spatial KWD for Large Spatial Maps

Contains efficient implementations of Discrete Optimal Transport algorithms for the computation of Kantorovich-Wasserstein distances between pairs of large spatial maps (Bassetti, Gualandi, Veneroni (2020), <doi:10.1137/19M1261195>). All the algorithms are based on an ad-hoc implementation of the Network Simplex algorithm. The package has four main helper functions: compareOneToOne() (to compare two spatial maps), compareOneToMany() (to compare a reference map with a list of other maps), compareAll() (to compute a matrix of distances between a list of maps), and focusArea() (to compute the KWD distance within a focus area). In non-convex maps, the helper functions first build the convex-hull of the input bins and pad the weights with zeros.

Version: 0.4.1
Imports: methods, Rcpp
LinkingTo: Rcpp
Published: 2022-12-09
DOI: 10.32614/CRAN.package.SpatialKWD
Author: Stefano Gualandi [aut, cre]
Maintainer: Stefano Gualandi <stefano.gualandi at>
NeedsCompilation: yes
SystemRequirements: C++11
CRAN checks: SpatialKWD results


Reference manual: SpatialKWD.pdf


Package source: SpatialKWD_0.4.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): SpatialKWD_0.4.1.tgz, r-oldrel (arm64): SpatialKWD_0.4.1.tgz, r-release (x86_64): SpatialKWD_0.4.1.tgz, r-oldrel (x86_64): SpatialKWD_0.4.1.tgz
Old sources: SpatialKWD archive


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